Both the number of viruses in initial flu infection, and the virus type, affects the patient's outcome. Mice infected by high concentrations developed immunity, and generated immune cells in the lungs to fight other strains. Mice that were infected with a relatively low concentration of the virus developed weaker immunity against the strain that infected them, did not build up this crucial population of immune cells in the lungs, and showed only delayed immunity toward other flu strains. This discovery could pave the way for new prophylactic strategies to fight flu infections and provides a novel basis for vaccine design. Learn more by clicking on the image or headline.

Most digital sensors are easy to connect, but those tucked away in hard-to-access spots will need to harvest ambient energy

ComplexInsight's insight:

Powering sensors over the long time periods is key to sensor network related developments in environmental monitoring and industrial internet of things. By recouping ambient energy as a power sources for sensors - deployed duration times can become measured in years and decades rather than weeks and months. Good article on changes in sensor power sources designers can expect to be available soon.

Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions---delivery failures, faulty parts, delays, power outages, theft, government failures---that systematically thwart the production process. To understand how these disruptions affect economic development, we model an evolving input--output network in which disruptions spread contagiously among optimizing agents. The key finding is that a poverty trap can emerge: agents adapt to frequent disruptions by producing simpler, less valuable goods, yet disruptions persist. Growing out of poverty requires that agents invest in buffers to disruptions. These buffers rise and then fall as the economy produces more complex goods, a prediction consistent with global patterns of input inventories. Large jumps in economic complexity can backfire. This result suggests why "big push" policies can fail, and it underscores the importance of reliability and of gradual increases in technological complexity.

CompleNet is an international conference that brings together researchers and practitioners from diverse disciplines—from sociology, biology, physics, and computer science—who share a passion to better understand the interdependencies within and across systems. CompleNet is a venue to discuss ideas and findings about all types networks, from biological, to technological, to informational and social. It is this interdisciplinary nature of complex networks that CompleNet aims to explore and celebrate.

One hundred and ten Zika virus genomes from ten countries and territories involved in the Zika virus epidemic reveal rapid expansion of the epidemic within Brazil and multiple introductions to other regions.

Biological organisms must perform computation as they grow, reproduce, and evolve. Moreover, ever since Landauer's bound was proposed it has been known that all computation has some thermodynamic cost -- and that the same computation can be achieved with greater or smaller thermodynamic cost depending on how it is implemented. Accordingly an important issue concerning the evolution of life is assessing the thermodynamic efficiency of the computations performed by organisms. This issue is interesting both from the perspective of how close life has come to maximally efficient computation (presumably under the pressure of natural selection), and from the practical perspective of what efficiencies we might hope that engineered biological computers might achieve, especially in comparison with current computational systems. Here we show that the computational efficiency of translation, defined as free energy expended per amino acid operation, outperforms the best supercomputers by several orders of magnitude, and is only about an order of magnitude worse than the Landauer bound. However this efficiency depends strongly on the size and architecture of the cell in question. In particular, we show that the {\it useful} efficiency of an amino acid operation, defined as the bulk energy per amino acid polymerization, decreases for increasing bacterial size and converges to the polymerization cost of the ribosome. This cost of the largest bacteria does not change in cells as we progress through the major evolutionary shifts to both single and multicellular eukaryotes. However, the rates of total computation per unit mass are nonmonotonic in bacteria with increasing cell size, and also change across different biological architectures including the shift from unicellular to multicellular eukaryotes.

The thermodynamic efficiency of computations made in cells across the range of lifeChristopher P. Kempes, David Wolpert, Zachary Cohen, Juan Pérez-Mercader

Is Chinese urbanization going to take a long time, or can its development goal be achieved by the government in a short time? What is the highest stable urbanization level that China can reach? When can China complete its urbanization? To answer these questions, this paper presents a system dynamic (SD) model of Chinese urbanization, and its validity and simulation are justified by a stock-flow test and a sensitivity analysis using real data from 1998 to 2013. Setting the initial conditions of the simulation by referring to the real data of 2013, the multi-scenario analysis from 2013 to 2050 reveals that Chinese urbanization will reach a level higher than 70% in 2035 and then proceed to a slow urbanization stage regardless of the population policy and GDP growth rate settings; in 2050, Chinese urbanization levels will reach approximately 75%, which is a stable and equilibrium level for China. Thus, it can be argued that Chinese urbanization is a long social development process that will require approximately 20 years to complete and that the ultimate urbanization level will be 75–80%, which means that in the distant future, 20–25% of China’s population will still settle in rural regions of China.

This web collection showcases the potential of interdisciplinary complexity research by bringing together a selection of recent Nature Communications articles investigating complex systems. Complexity research aims to characterize and understand the behaviour and nature of systems made up of many interacting elements. Such efforts often require interdisciplinary collaboration and expertise from diverse schools of thought. Nature Communications publishes papers across a broad range of topics that span the physical and life sciences, making the journal an ideal home for interdisciplinary studies.

The 1918 influenza pandemic probably infected one-third of the world's population at the time — 500 million people. It killed between 50 million and 100 million; by contrast, Second World War deaths numbered around 60 million. Why is this catastrophe

Gene transfer is seen as a hopeful therapy for Alzheimer's and Parkinson's patients. The approach involves using harmless laboratory-produced viruses to introduce important genes into the brain cells. In a study on mice

There are more than thirty thousand species of fish―more than mammals, birds, reptiles, and amphibians combined. But for all their breathtaking diversity and beauty, we rarely consider how fish think, feel, and behave. In What a Fish Knows, the ethologist Jonathan Balcombe takes us under the sea and to the other side of the aquarium glass to reveal what fishes can do, how they do it, and why. Introducing the latest revelations in animal behavior and biology, Balcombe upends our assumptions about fish, exposing them not as unfeeling, dead-eyed creatures but as sentient, aware, social―even Machiavellian. They conduct elaborate courtship rituals and develop lifelong bonds with shoal-mates. They also plan, hunt cooperatively, use tools, punish wrongdoers, curry favor, and deceive one another. Fish possess sophisticated senses that rival our own. The reef-dwelling damselfish identifies its brethren by face patterns visible only in ultraviolet light, and some species communicate among themselves in murky waters using electric signals. Highlighting these breakthrough discoveries and others from his own encounters with fish, Balcombe inspires a more enlightened appraisal of marine life.

An illuminating journey into the world of underwater science, What a Fish Knows will forever change your view of our aquatic cousins.

The majority of human emerging infectious diseases are zoonotic, with viruses that originate in wild mammals of particular concern (for example, HIV, Ebola and SARS). Understanding patterns of viral diversity in wildlife and determinants of successful cross-species transmission, or spillover, are therefore key goals for pandemic surveillance programs. However, few analytical tools exist to identify which host species are likely to harbour the next human virus, or which viruses can cross species boundaries. Here we conduct a comprehensive analysis of mammalian host–virus relationships and show that both the total number of viruses that infect a given species and the proportion likely to be zoonotic are predictable. After controlling for research effort, the proportion of zoonotic viruses per species is predicted by phylogenetic relatedness to humans, host taxonomy and human population within a species range—which may reflect human–wildlife contact. We demonstrate that bats harbour a significantly higher proportion of zoonotic viruses than all other mammalian orders. We also identify the taxa and geographic regions with the largest estimated number of ‘missing viruses’ and ‘missing zoonoses’ and therefore of highest value for future surveillance. We then show that phylogenetic host breadth and other viral traits are significant predictors of zoonotic potential, providing a novel framework to assess if a newly discovered mammalian virus could infect people.

Understanding zoonotic potential will be key to health planning and epidemic prevention in the 21st century. This paper has key insights such as major hosts (bats) and key geographic zones for observation. If you are involved in health planning or disease modeling - very worthwhile reading.

Computer models can help humans gain insight into the functioning of complex systems. Used for training, they can also help gain insight into the cognitive processes humans use to understand these systems. By influencing humans understanding (and consequent actions) computer models can thus generate an impact on both these actors and the very systems they are designed to simulate. When these systems also include humans, a number of self-referential relations thus emerge which can lead to very complex dynamics. This is particularly true when we explicitly acknowledge and model the existence of multiple conflicting representations of reality among different individuals. Given the increasing availability of computational devices, the use of computer models to support individual and shared decision making could potentially have implications far wider than the ones often discussed within the Information and Communication Technologies community in terms of computational power and network communication. We discuss some theoretical implications and describe some initial numerical simulations.

Models and people: An alternative view of the emergent properties of computational models Fabio Boschetti

Network science offers a powerful language to represent and study complex systems composed of interacting elements from the Internet to social and biological systems. In its standard formulation, this framework relies on the assumption that the underlying topology is static, or changing very slowly as compared to dynamical processes taking place on it, e.g., epidemic spreading or navigation. Fuelled by the increasing availability of longitudinal networked data, recent empirical observations have shown that this assumption is not valid in a variety of situations. Instead, often the network itself presents rich temporal properties and new tools are required to properly describe and analyse their behaviour.A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks, and provides connections between different areas of knowledge required to address this multi-disciplinary subject. After an introduction to key concepts on networks and stochastic dynamics, the authors guide the reader through a coherent selection of mathematical and computational tools for network dynamics. Perfect for students and professionals, this book is a gateway to an active field of research developing between the disciplines of applied mathematics, physics and computer science, with applications in others including social sciences, neuroscience and biology.

We present a model of contagion that unifies and generalizes existing models of the spread of social influences and micro-organismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g., a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the so-called SIS model). We identify three basic classes of contagion models which we call \textit{epidemic threshold}, \textit{vanishing critical mass}, and \textit{critical mass} classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory.

A generalized model of social and biological contagionPeter Sheridan Dodds, Duncan J. Watts

In this paper we present a thorough analysis of the nature of news in different mediums across the ages, introducing a unique mathematical model to fit the characteristics of information spread. This model enhances the information diffusion model to account for conflicting information and the topical distribution of news in terms of popularity for a given era. We translate this information to a separate graphical node model to determine the spread of a news item given a certain category and relevance factor. The two models are used as a base for a simulation of information dissemination for varying graph topoligies. The simulation is stress-tested and compared against real-world data to prove its relevancy. We are then able to use these simulations to deduce some conclusive statements about the optimization of information spread.

Characterizing information importance and the effect on the spread in various graph topologiesJames Flamino, Alexander Norman, Madison Wyatt

How cooperation can evolve between players is an unsolved problem of biology. Here we use Hamiltonian dynamics of models of the Ising type to describe populations of cooperating and defecting players to show that the equilibrium fraction of cooperators is given by the expectation value of a thermal observable akin to a magnetization. We apply the formalism to the Public Goods game with three players, and show that a phase transition between cooperation and defection occurs that is equivalent to a transition in one-dimensional Ising crystals with long-range interactions. We also investigate the effect of punishment on cooperation and find that punishment acts like a magnetic field that leads to an "alignment" between players, thus encouraging cooperation. We suggest that a thermal Hamiltonian picture of the evolution of cooperation can generate other insights about the dynamics of evolving groups by mining the rich literature of critical dynamics in low-dimensional spin systems.

Advancements in science and technology take place on a global scale without much consideration of the exact implications that they may essentially have on the species or our planet. Over the last few decades, things are moving very fast and not always in a good way. The climate of the planet is changing drastically. Ice caps are melting faster than ever. Known animal species around the world are declining at rates faster than ever previously known in recorded history. We humans, might have intelligent individuals amidst us. However, collectively, to any external observer, we would perhaps seem to act more like mindless scavengers stripping the planet of its resources faster than she can ever replenish them. And this all seems to be intrinsically linked with our seemingly insatiable “collective” urge to satisfy immediate needs. So, while the technological revolution has greatly benefited humankind, our continual reliance on technology also has considerable collateral effects on the planet.

This year marks the tenth anniversary of the identification of the biological function of CRISPR–Cas as adaptive immune systems in bacteria. In just a decade, the characterization of CRISPR–Cas systems has established a novel means of adaptive immunity in bacteria and archaea and deepened our understanding of the interplay between prokaryotes and their environment, and CRISPR-based molecular machines have been repurposed to enable a genome editing revolution. Here, we look back on the historical milestones that have paved the way for the discovery of CRISPR and its function, and discuss the related technological applications that have emerged, with a focus on microbiology. Lastly, we provide a perspective on the impacts the field has had on science and beyond.

Biological organisms must perform computation as they grow, reproduce, and evolve. Moreover, ever since Landauer's bound was proposed it has been known that all computation has some thermodynamic cost -- and that the same computation can be achieved with greater or smaller thermodynamic cost depending on how it is implemented. Accordingly an important issue concerning the evolution of life is assessing the thermodynamic efficiency of the computations performed by organisms. This issue is interesting both from the perspective of how close life has come to maximally efficient computation (presumably under the pressure of natural selection), and from the practical perspective of what efficiencies we might hope that engineered biological computers might achieve, especially in comparison with current computational systems. Here we show that the computational efficiency of translation, defined as free energy expended per amino acid operation, outperforms the best supercomputers by several orders of magnitude, and is only about an order of magnitude worse than the Landauer bound. However this efficiency depends strongly on the size and architecture of the cell in question. In particular, we show that the {\it useful} efficiency of an amino acid operation, defined as the bulk energy per amino acid polymerization, decreases for increasing bacterial size and converges to the polymerization cost of the ribosome. This cost of the largest bacteria does not change in ancells as we progress through the major evolutionary shifts to both single and multicellular eukaryotes. However, the rates of total computation per unit mass are nonmonotonic in bacteria with increasing cell size, and also change across different biological architectures including the shift from unicellular to multicellular eukaryotes.

The thermodynamic efficiency of computations made in cells across the range of lifeChristopher P. Kempes, David Wolpert, Zachary Cohen, Juan Pérez-Mercader

You might think that plants grow according to how much nutrition, water and sunlight they are exposed to, but new research... shows that the plant's own genetics may be the real limiting factor.

"This could have potentially big implications for the agricultural industry... Our model plant is in the same family as cabbages, so it's easy to imagine creating giant cabbages or growing them to the desired market size faster than at present."

It was previously assumed that plant growth was generally resource-limited, meaning that plants would only grow as large and fast as they could photosynthesise. However, Dr Pullen and his team present evidence that plant growth is actually "sink-limited", meaning that genetic regulation and cell division rates have a much bigger role in controlling plant growth than previously thought:

"We are proposing that plant growth is not physically limited by Net Primary Productivity (NPP) or the environment, but instead is limited genetically in response to these signals to ensure they do not become limiting."

By genetically altering the growth repressors in Arabidopsis, Dr Pullen and his team were able to create mutant strains. They identified the metabolic rates of the different plant strains... as well as comparing the size and weight of the plants... also grew the mutant plant strains at different temperatures to see if this changed their results: "When grown at different temperatures we still find a difference in size"...

The impact of these results is wide-reaching, and... it may even change how we think about global climate data: "Climate models need to incorporate genetic elements because at present most do not, and their predictions would be much improved with a better understanding of plant carbon demand."

Genetic disease affecting vision can significantly impact patient quality of life. Gene therapy seeks to slow the progression of these diseases by treating the underlying etiology at the level of the genome. Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated systems (Cas) represent powerful tools for studying diseases through the creation of model organisms generated by targeted modification and by the correction of disease mutations for therapeutic purposes. CRISPR-Cas systems have been applied successfully to the visual sciences and study of ophthalmic disease - from the modification of zebrafish and mammalian models of eye development and disease, to the correction of pathogenic mutations in patient-derived stem cells. Recent advances in CRISPR-Cas delivery and optimization boast improved functionality that continues to enhance genome-engineering applications in the eye. This review provides a synopsis of the recent implementations of CRISPR-Cas tools in the field of ophthalmology.

While the promise of CRISPR was not that it would change how genetics and biology behaved its quickly becoming hyped that way in popular press. The fact that the Cas9 protein can be used as a cheap and fast technique to co-localize with specific DNA sequences is undoubtedly incredibly useful. As researchers discover mechanisms where they can exploit co-localization and modification at specific sites using DNA cleavage capabilities it is important we get well informed reviews of actual applications as well as coverage of potential ones. This paper gives a good summary of recent developments and insights to applications of CRISPR-CAS in ophthalmology for both better understanding visual systems and potential treatments for ophthalmic disease.

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